Skip to main content
Graph
Search
fr
|
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Multivariate Statistics: Introduction and Methods
Graph Chatbot
Related lectures (31)
Previous
Page 1 of 4
Next
Multivariate Statistics: Normal Distribution
Introduces multivariate statistics, covering normal distribution properties and characteristic functions.
Multivariate Statistics: Normal Distribution
Covers the multivariate normal distribution, properties, and sampling methods.
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Multivariate Statistics: Wishart and Hotelling T²
Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Multivariate Statistics: Introduction and Methods
Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Principal Component Analysis: Theory and Applications
Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.
Dependence in Random Vectors
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
